Improving the Performance of Forecasting Models with Classical Statistical and Intelligent Models in Industrial Productions

Subject Areas :

Maryam Bahrami ¹ , Mehdi Khashei ² , Atefeh Amindoust ³

1 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran|Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
3 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Received: 2021-05-19 Accepted : 2021-08-01 Published : 2021-05-01

Keywords:

References:

[1] Shen., Z.M. and Daskin, M.S. 2005. Trade-offs between customer service and cost supply chain design. Manufacturing and service operations Management. 7(3):188-207.

[2] Armstrong, J. 2001. Principles of forecasting. Kluwer Academic Publisher, Dordrecht.

[3] Cassettari, L., Bendato, I., Mosca, M. and Mosca, R.  2017. A new stochastic multi source approach to improve the accuracy of the sales forecasts, Emerald Insight. 19(1): 48–64.

[4] Khashei, M., and Bijari, M. 2011. A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Applied Soft Computing. 11:2664– 2675.

[5] Duerr, I., Merrill, H.R., Wang, Ch., Bai, R., Boyer, M., Dukes, M. D. and Bliznyuk. N. 2018. Forecasting urban household water demand with statistical and machine learning methods using large space-time data: a comparative study. Environmental Modelling & Software. 102:29-38.

[6] Ahmad, T. and Chen, H. 2018. Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment. Energy. 160:1008-1020.

[7] Ahmad, T. and Chen, H. 2018. Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches. journal of Energy & Buildings. 166: 460–476.

[8] Candelieri, A., Giordani, I., Archetti, F., Barkalov, K., Meyerov, I., Polovinkin, A., Sysoyev, A. and Zolotykh, N. 2019. Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization. Computers and Operations Researc. 106: 202-209.

[9] Boutselis, P. and Mc Naught, k. 2019. Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context. International Journal of Production Economics. 209: 325-333.

[10] Ahmad, T. and Chen, H. 2018. Utility companies strategy for short-term energy demand forecasting using machine learning based models.  Sustainable Cities and Society. 39: 401–417.

[11] Cam, M., Zmeureanu, R. and Daoud, A. 2017. Cascade-based short-term forecasting method of the electric demand of HVAC system. Energy. 119: 1098-1107.

[12] Bikcora, C., Verheijen, L. and Weiland., S. 2018. Density forecasting of daily electricity demand with ARMA-GARCH, CAViaR, and CARE econometric models. Sustainable Energy, Grids and Networks. 13:148–156.

[13] Han,Y., Wang, L., Gao, J., Xing, Z. and Tao, T. 2017. Combination Forecasting Based on SVM and Neural Network for Urban Rail Vehicle Spare parts Demand. Proceedings of the 36^th Chinese Control Conference. July 26-28. 4660-4665.

[14] Arunraj, N. and Ahrens, D.  2015. A Hybrid Seasonal Autoregressive Integrated Moving Average and Quantile Regression for Daily Food Sales Forecasting. International Journal of Production Economics. 170(Part A):321-335.

[15] Mohammadi,R., Fatemi Ghomi, S.M.T. and Zeinali. F. 2014. A new hybrid evolutionary based RBF networks method for forecasting time series: A case study of forecasting emergency supply demand time series. Engineering Applications of Artificial Intelligence. 36: 204–214.

[16] Aburto, l. and Weber, R. 2007. Improved supply chain management based on hybrid demand forecasts. journal of Applied Soft Computing. 7: 136–144.

[17] Al-Fattah, S. M. and Aramco, S. 2021. Application of the artificial intelligence GANNATS model in forecasting crude oil demand for Saudi Arabia and China. Journal of Petroleum Science and Engineering.  200:108368.

[18] Sharma, S. K. and George, S., A. 2021. A vendor recommendation model using neural networks. International Journal of Process Management and Benchmarking. 11(2): 254 – 270.

[19] Hess, A., Spinler, S. and Winkenbach, M., 2020. Real-time demand forecasting for an urban delivery platform. 145: 1-20.

[20] Pérez-Chacón, R., Asencio-Cortés, G., Martínez-Álvarez, F. and Troncoso, A. 2020. Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand. Information Sciences. 540: 160-174.

[21] Van Steenbergen, R., M. and Mes, M., R., K. 2020. Forecasting demand profiles of new products. Decision Support Systems. 139(113401): 1-15, 2020.

[22] Oey, E., Wijaya, W. A. and Hansopaheluwakan, S. 2020. Forecasting and aggregate planning application – a case study of a small enterprise in Indonesia. International Journal of Process Management and Benchmarking. 10(1): 1 – 21.

[23] Xu, S., Chan, H, K. and Zhang, T. 2019. Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach. Transportation Research. Part E, 122:169–180.

[24] Box, P. and Jenkins, G.M. 1976. Time Series Analysis: Forecasting and Control. Holden-day Inc, San Francisco, CA.

[25] Cybenko, G. 1989. Approximation by superposition of a sigmoidal function, Mathematical Control Signal Systems. 2:303–314.

[26] Hornik, K., Stinchcombe, M. and White, H. 1989. Multilayer feed-forward networks are universal approximators. Neural Networks. 2:359–366.

[27] Funahashi, K. 1989. On the approximate realization of continuous mappings by neural networks. Neural Networks. 2:183–192.

[28] Zhang, G., P. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 50:159 – 175.

[29] Tseng, F.M., Yu, H.C. and Tzeng, G.H. 2002. Combining neural network model with seasonal time series ARIMA model. Technological Forecasting & Social Change. 69: 71–87.

[30] Tseng, M., Tzeng, G.H. and Yu, HC. 1999. Fuzzy Seasonal Time Series for Forecasting the Production Value of the Mechanical Industry in Taiwan. Technological Forecasting and Social Change. 60: 263–273.

[31] Montgomery, D.C., Johnson, L.A. and Gardiner, J.S. 1990. Forecasting and Time Series Analysis.  McGraw-Hill, New York.